Articles | Volume 26, issue 8
https://doi.org/10.5194/hess-26-2245-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2245-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 May 2022
Research article |
| 02 May 2022
| 02 May 2022
The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Xingyuan Chen
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Utkarsh Mital
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Ethan T. Coon
Climate Change Science Institute & Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Dipankar Dwivedi
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Related authors
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
Short summary
Short summary
We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
Alexandra Hamm, Erik Schytt Mannerfelt, Aaron A. Mohammed, Scott L. Painter, Ethan T. Coon, and Andrew Frampton
The Cryosphere, 19, 3693–3724, https://doi.org/10.5194/tc-19-3693-2025, https://doi.org/10.5194/tc-19-3693-2025, 2025
Short summary
Short summary
The fate of thawing permafrost carbon is essential for understanding the permafrost–climate feedback and projections of future climate. Here we study transport of organic carbon by groundwater in the active layer of a hillslope model. We find that carbon transport velocities and microbial mineralization rates are strongly dependent on liquid saturation in the seasonally thawed active layer. In a warming climate, the rate at which permafrost thaws determines how fast carbon can be transported.
Mingjie Shi, Nate McDowell, Huilin Huang, Faria Zahura, Lingcheng Li, and Xingyuan Chen
Biogeosciences, 22, 2225–2238, https://doi.org/10.5194/bg-22-2225-2025, https://doi.org/10.5194/bg-22-2225-2025, 2025
Short summary
Short summary
Using Moderate Resolution Imaging Spectroradiometer data products, we quantitatively estimate the resistance and resilience of ecosystem functions to wildfires that occurred in the Columbia River basin in 2015. The carbon state exhibits lower resistance and resilience than the ecosystem fluxes. The random forest feature importance analysis indicates that burn severity plays a minor role in the resilience of grassland and a relatively major role in the resilience of forest and savanna.
Maggi M. Laan, Stephanie G. Fulton, Vanessa A. Garayburu-Caruso, Morgan E. Barnes, Mikayla A. Borton, Xingyuan Chen, Yuliya Farris, Brieanne Forbes, Amy E. Goldman, Samantha Grieger, Robert O. Hall Jr., Matthew H. Kaufman, Xinming Lin, Erin L. M. Zionce, Sophia A. McKever, Allison Myers-Pigg, Opal Otenburg, Aaron C. Pelly, Huiying Ren, Lupita Renteria, Timothy D. Scheibe, Kyongho Son, Jerry Tagestad, Joshua M. Torgeson, and James C. Stegen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1109, https://doi.org/10.5194/egusphere-2025-1109, 2025
Short summary
Short summary
Respiration is a process that combines carbon and oxygen to generate energy for living organisms. Within a river, respiration in sediments and water have variable contributions to respiration of the whole river system. Contrary to conventional wisdom, we found that water column respiration did not increase systematically moving from small streams to big rivers. Instead, it was locally influenced by temperature, nutrients and suspended solids.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
James Stegen, Amy J. Burgin, Michelle H. Busch, Joshua B. Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate McDowell, Corianne Tatariw, Anna Braswell, Jillian M. Deines, Julia A. Guimond, Peter Regier, Kenton Rod, Edward K. P. Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin L. Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon N. Sweetman, Jianqiu Zheng, Daniel C. Allen, Elizabeth Herndon, Beth A. Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, and Kaizad F. Patel
Biogeosciences, 22, 995–1034, https://doi.org/10.5194/bg-22-995-2025, https://doi.org/10.5194/bg-22-995-2025, 2025
Short summary
Short summary
The loss and gain of surface water (variable inundation) are common processes across Earth. Global change shifts variable inundation dynamics, highlighting a need for unified understanding that transcends individual variably inundated ecosystems (VIEs). We review the literature, highlight challenges, and emphasize opportunities to generate transferable knowledge by viewing VIEs through a common lens. We aim to inspire the emergence of a cross-VIE community based on a proposed continuum approach.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Radhakrishna Bangalore Lakshmiprasad, Fan Zhang, Ethan T. Coon, and Thomas Graf
EGUsphere, https://doi.org/10.5194/egusphere-2023-3122, https://doi.org/10.5194/egusphere-2023-3122, 2024
Preprint archived
Short summary
Short summary
An effective method of understanding permafrost dynamics due to climate change is numerical modeling. The research work established a novel numerical approach to assess the required level of surface process complexity and set up a numerical model at the Yakou catchment in the Qinghai-Tibet Plateau. The main research findings were that permafrost thawing was not well represented by considering only subsurface processes, and liquid precipitation increased the rate of permafrost degradation.
Stephanie G. Fulton, Morgan Barnes, Mikayla A. Borton, Xingyuan Chen, Yuliya Farris, Brieanne Forbes, Vanessa A. Garayburu-Caruso, Amy E. Goldman, Samantha Grieger, Robert Hall Jr., Matthew H. Kaufman, Xinming Lin, Erin McCann, Sophia A. McKever, Allison Myers-Pigg, Opal C. Otenburg, Aaron C. Pelly, Huiying Ren, Lupita Renteria, Timothy D. Scheibe, Kyongho Son, Jerry Tagestad, Joshua M. Torgeson, and James C. Stegen
EGUsphere, https://doi.org/10.5194/egusphere-2023-3038, https://doi.org/10.5194/egusphere-2023-3038, 2024
Preprint archived
Short summary
Short summary
This research examines oxygen use in rivers, which is central to the carbon cycle and water quality. The study focused on an environmentally diverse river basin in the western United States and found that oxygen use in river water was very slow and influenced by factors like water temperature and concentrations of nutrients and carbon in the water. Results suggest that in the study system, most of the oxygen use occurs via mechanisms directly or indirectly associated with riverbed sediments.
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
Short summary
Short summary
We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
Utkarsh Mital, Dipankar Dwivedi, James B. Brown, and Carl I. Steefel
Earth Syst. Sci. Data, 14, 4949–4966, https://doi.org/10.5194/essd-14-4949-2022, https://doi.org/10.5194/essd-14-4949-2022, 2022
Short summary
Short summary
We present a new dataset that estimates small-scale variations in precipitation and temperature in mountainous terrain. The dataset is generated using a new machine learning framework that extracts relationships between climate and topography from existing coarse-scale datasets. The generated dataset is shown to capture small-scale variations more reliably than existing datasets and constitutes a valuable resource to model the water cycle in the mountains of Colorado, western United States.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
Short summary
Short summary
High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
Bo Gao and Ethan T. Coon
The Cryosphere, 16, 4141–4162, https://doi.org/10.5194/tc-16-4141-2022, https://doi.org/10.5194/tc-16-4141-2022, 2022
Short summary
Short summary
Representing water at constant density, neglecting cryosuction, and neglecting heat advection are three commonly applied but not validated simplifications in permafrost models to reduce computation complexity at field scale. We investigated this problem numerically by Advanced Terrestrial Simulator and found that neglecting cryosuction can cause significant bias (10%–60%), constant density primarily affects predicting water saturation, and ignoring heat advection has the least impact.
Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/hess-26-1727-2022, https://doi.org/10.5194/hess-26-1727-2022, 2022
Short summary
Short summary
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
Short summary
Short summary
It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for
ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 2013. a
Alemohammad, S. H., McColl, K. A., Konings, A. G., Entekhabi, D., and
Stoffelen, A.: Characterization of precipitation product errors across the
United States using multiplicative triple collocation, Hydrol. Earth Syst. Sci., 19, 3489–3503, https://doi.org/10.5194/hess-19-3489-2015, 2015. a
Aquanty, I.: HydroGeoSphere User Manual, Waterloo, Ontario, https://www.aquanty.com/hgs-download (last access: 28 April 2022), 2015. a
Behnke, R., Vavrus, S., Allstadt, A., Albright, T., Thogmartin, W. E., and
Radeloff, V. C.: Evaluation of downscaled, gridded climate data for the
conterminous United States, Ecol. Appl., 26, 1338–1351, https://doi.org/10.1002/15-1061, 2016. a, b, c, d
Bolton, D.: The Computation of Equivalent Potential Temperature, Mon. Weather Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2, 1980. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Bruni, G., Reinoso, R., Van De Giesen, N. C., Clemens, F. H., and Ten Veldhuis, J. A.: On the sensitivity of urban hydrodynamic modelling to
rainfall spatial and temporal resolution, Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, 2015. a
Coon, E. T. and Shuai, P.: Watershed Workflow, [Computer Software],
https://doi.org/10.11578/dc.20211008.1, 2021. a
Coon, E. T., Svyatskiy, D., Jan, A., Kikinzon, E., Berndt, M., Atchley, A. L., Harp, D. R., Manzini, G., Shelef, E., Lipnikov, K., Garimella, R., Xu, C., Moulton, J. D., Karra, S., Painter, S. L., Jafarov, E., and Molins, S.:
Advanced Terrestrial Simulator (ATS), US DOE Office of Science (SC),
Biological and Environmental Research (BER), https://doi.org/10.11578/dc.20190911.1,
2019. a, b, c
Coon, E. T., Moulton, J. D., Kikinzon, E., Berndt, M., Manzini, G., Garimella, R., Lipnikov, K., and Painter, S. L.: Coupling surface flow and subsurface flow in complex soil structures using mimetic finite differences, Adv. Water Resour., 144, 103701, https://doi.org/10.1016/j.advwatres.2020.103701, 2020. a
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F.,
Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L.,
Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Meng, J.: Real-time and
retrospective forcing in the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res.-Atmos., 108, 8842, https://doi.org/10.1029/2002jd003118, 2003. a, b
Cromwell, E., Shuai, P., Jiang, P., Coon, E. T., Painter, S. L., Moulton, J. D., Lin, Y., and Chen, X.: Estimating Watershed Subsurface Permeability
From Stream Discharge Data Using Deep Neural Networks, Front. Earth Sci., 9, 1–13, https://doi.org/10.3389/feart.2021.613011, 2021. a
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor,
G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping
of climatological temperature and precipitation across the conterminous
United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008. a, b, c, d, e, f, g
Elsner, M. M., Gangopadhyay, S., Pruitt, T., Brekke, L. D., Mizukami, N., and
Clark, M. P.: How Does the Choice of Distributed Meteorological Data Affect
Hydrologic Model Calibration and Streamflow Simulations?, J. Hydrometeorol., 15, 1384–1403, https://doi.org/10.1175/jhm-d-13-083.1, 2014. a, b
Eum, H. I., Dibike, Y., Prowse, T., and Bonsal, B.: Inter-comparison of
high-resolution gridded climate data sets and their implication on hydrological model simulation over the Athabasca Watershed, Canada, Hydrol. Process., 28, 4250–4271, https://doi.org/10.1002/hyp.10236, 2014. a
Ficchì, A., Perrin, C., and Andréassian, V.: Impact of temporal
resolution of inputs on hydrological model performance: An analysis based on
2400 flood events, J. Hydrol., 538, 454–470, https://doi.org/10.1016/j.jhydrol.2016.04.016, 2016. a
Gao, J., Sheshukov, A. Y., Yen, H., and White, M. J.: Impacts of alternative
climate information on hydrologic processes with SWAT: A comparison of NCDC,
PRISM and NEXRAD datasets, Catena, 156, 353–364, https://doi.org/10.1016/j.catena.2017.04.010, 2017. a
Gatzke, S. E., Beaudette, D. E., Ficklin, D. L., Luo, Y., O'Geen, A. T., and
Zhang, M.: Aggregation Strategies for SSURGO Data: Effects on SWAT Soil
Inputs and Hydrologic Outputs, Soil Sci. Soc. Am. J., 75, 1908–1921, https://doi.org/10.2136/sssaj2010.0418, 2011. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Huscroft, J., Gleeson, T., Hartmann, J., and Börker, J.: Compiling and
Mapping Global Permeability of the Unconsolidated and Consolidated Earth:
GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0), Geophys. Res. Lett., 45, 1897–1904, https://doi.org/10.1002/2017GL075860, 2018. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a, b
Ko, A., Mascaro, G., and Vivoni, E. R.: Strategies to Improve and Evaluate
Physics-Based Hyperresolution Hydrologic Simulations at Regional Basin
Scales, Water Resour. Res., 55, 1129–1152, https://doi.org/10.1029/2018WR023521, 2019. a, b
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow
modeling: A free-surface overland flow boundary condition in a parallel
groundwater flow model, Adv. Water Resour., 29, 945–958,
https://doi.org/10.1016/j.advwatres.2005.08.006, 2006. a
Koppen, W. and Geiger, R.: Handbook of climatology, vol. 1, Gebruder
Borntraeger, Berlin, 1930. a
Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G. S.: A note on
leveraging synergy in multiple meteorological data sets with deep learning
for rainfall–runoff modeling, Hydrol. Earth Syst. Sci., 25, 2685–2703, https://doi.org/10.5194/hess-25-2685-2021, 2021. a
Loheide, S. P. and Lundquist, J. D.: Snowmelt-induced diel fluxes through the
hyporheic zone, Water Resour. Rese., 45, W07404, https://doi.org/10.1029/2008WR007329, 2009. a
Maina, F. Z., Siirila-Woodburn, E. R., and Vahmani, P.: Sensitivity of
meteorological-forcing resolution on hydrologic variables, Hydrol. Earth Syst. Sci., 24, 3451–3474, https://doi.org/10.5194/hess-24-3451-2020, 2020. a, b, c
Maxwell, R. M. and Condon, L. E.: Connections between groundwater flow and
transpiration partitioning, Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891, 2016. a, b
Mital, U., Dwivedi, D., Brown, J., and Steefel, C.:: Downscaled precipitation and mean air temperature datasets; East-Taylor subbasin; 2008–2019; daily temporal resolution; 400 m spatial resolution, ExaSheds, ESS-DIVE repository [data set], https://doi.org/10.15485/1822259, 2021. a
Mital, U., Dwivedi, D., Brown, J. B., and Steefel, C. I.: Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for East Taylor subbasin (western United States), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-67, in review, 2022. a
Mitchell, K. E.: The multi-institution North American Land Data Assimilation
System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system, J. Geophys. Res., 109, D07S90, https://doi.org/10.1029/2003JD003823, 2004. a
Mourtzinis, S., Rattalino Edreira, J. I., Conley, S. P., and Grassini, P.: From grid to field: Assessing quality of gridded weather data for agricultural applications, Eur. J. Agron., 82, 163–172, https://doi.org/10.1016/j.eja.2016.10.013, 2017. a
Muche, M. E., Sinnathamby, S., Parmar, R., Knightes, C. D., Johnston, J. M.,
Wolfe, K., Purucker, S. T., Cyterski, M. J., and Smith, D.: Comparison and
Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural
Watershed Using SWAT, J. Am. Water Resour. Assoc., 56, 486–506, https://doi.org/10.1111/1752-1688.12819, 2020. a, b, c, d, e
Ochoa-Rodriguez, S., Wang, L. P., Gires, A., Pina, R. D., Reinoso-Rondinel, R., Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., Van Assel, J., Kroll, S., Murlà-Tuyls, D., Tisserand, B., Schertzer, D., Tchiguirinskaia, I.,
Onof, C., Willems, P., and Ten Veldhuis, M. C.: Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation, J. Hydrol., 531, 389–407, https://doi.org/10.1016/j.jhydrol.2015.05.035, 2015. a, b, c
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z. M., Swenson, S., Thornton, P. E.,
Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S. P., Ricciuto, D. M.,
Sacks, W. J., Sun, Y., Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM), No. NCAR/TN-503+STR, NCAR, p. D6RR1W7M, https://doi.org/10.5065/D6RR1W7M, 2013. a
Pan, M., Sheffield, J., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake,
J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., and Tarpley, J. D.: Snow process modeling in the North
American Land Data Assimilation System (NLDAS): 1. Evaluation of model simulated snow water equivalent, J. Geophys. Res.-Atmos., 108, 1–14, https://doi.org/10.1029/2003jd003994, 2003. a
Pan, M., Cai, X., Chaney, N. W., Entekhabi, D., and Wood, E. F.: An initial
assessment of SMAP soil moisture retrievals using high-resolution model
simulations and in situ observations, Geophys. Res. Lett., 43, 9662–9668, https://doi.org/10.1002/2016GL069964, 2016. a
Petrone, K., Buffam, I., and Laudon, H.: Hydrologic and biotic control of
nitrogen export during snowmelt: A combined conservative and reactive tracer
approach, Water Resour. Res., 43, 1–13, https://doi.org/10.1029/2006WR005286, 2007. a
Schreiner‐McGraw, A. P. and Ajami, H.: Impact of Uncertainty in Precipitation Forcing Data Sets on the Hydrologic Budget of an Integrated Hydrologic Model in Mountainous Terrain, Water Resour. Res., 56, 1–21, https://doi.org/10.1029/2020WR027639, 2020. a, b
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.: Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Syst., 9, 65–88, https://doi.org/10.1002/2016MS000686, 2017. a
Sheffield, J., Pan, M., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake,
J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Ramsay, B. H.: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent, J. Geophys. Res.-Atmos., 108, 1–13, https://doi.org/10.1029/2002jd003274, 2003. a
Shuai, P., Cardenas, M. B., Knappett, P. S. K., Bennett, P. C., and Neilson,
B. T.: Denitrification in the banks of fluctuating rivers: The effects of river stage amplitude, sediment hydraulic conductivity and dispersivity, and
ambient groundwater flow, Water Resour. Res., 53, 7951–7967, https://doi.org/10.1002/2017WR020610, 2017. a
Shuai, P., Chen, X., Mital, U., Coon, E., and Dwivedi, D.: Data-model files associated with the manuscript “The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses” (Shuai et al., 2022 HESS), ExaSheds, ESS-DIVE repository [data set], https://doi.org/10.15485/1861432, 2022. a
Song, X., Chen, X., Stegen, J., Hammond, G., Song, H.-S., Dai, H., Graham, E., and Zachara, J. M.: Drought Conditions Maximize the Impact of High-Frequency Flow Variations on Thermal Regimes and Biogeochemical Function in the Hyporheic Zone, Water Resour. Res., 54, 7361–7382,
https://doi.org/10.1029/2018WR022586, 2018. a
Staudinger, M., Stoelzle, M., Cochand, F., Seibert, J., Weiler, M., and
Hunkeler, D.: Your work is my boundary condition!: Challenges and approaches
for a closer collaboration between hydrologists and hydrogeologists, J. Hydrol., 571, 235–243, https://doi.org/10.1016/j.jhydrol.2019.01.058, 2019. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a
Thornton, P. E., Running, S. W., and White, M. A.: Generating surfaces of
daily meteorological variables over large regions of complex terrain, J. Hydrol., 190, 214–251, https://doi.org/10.1016/S0022-1694(96)03128-9, 1997. a, b, c, d
Thornton, P. E., Shrestha, R., Thornton, M., Kao, S.-C., Wei, Y., and Wilson,
B. E.: Gridded daily weather data for North America with comprehensive
uncertainty quantification, Scient. Data, 8, 1–17, https://doi.org/10.1038/s41597-021-00973-0, 2021. a, b, c, d
Wetterhall, F., He, Y., Cloke, H., and Pappenberger, F.: Effects of temporal
resolution of input precipitation on the performance of hydrological forecasting, Adv. Geosci., 29, 21–25, https://doi.org/10.5194/adgeo-29-21-2011, 2011. a
Woelber, B., Maneta, M. P., Harper, J., Jencso, K. G., Payton Gardner, W.,
Wilcox, A. C., and López-Moreno, I.: The influence of diurnal snowmelt and transpiration on hillslope throughflow and stream response, Hydrol. Earth Syst. Sci., 22, 4295–4310, https://doi.org/10.5194/hess-22-4295-2018, 2018.
a
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B.,
Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P.: Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011. a
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L., Alonge,
C., Wei, H., Meng, J., Livneh, B., Duan, Q., and Lohmann, D.: Continental-scale water and energy flux analysis and validation for North
American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow, J. Geophys. Res.-Atmos., 117, D03110, https://doi.org/10.1029/2011JD016051, 2012. a, b
Zhang, Y. and Schaap, M. G.: Weighted Recalibration of the Rosetta Pedotransfer Model with Improved Estimates of Hydraulic Parameter Distributions and Summary Statistics (Rosetta3), J. Hydrol., 547, 39–53, https://doi.org/10.1016/j.jhydrol.2017.01.004, 2017. a
Short summary
Using an integrated watershed model, we compared simulated watershed hydrologic variables driven by three publicly available gridded meteorological forcings (GMFs) at various spatial and temporal resolutions. Our results demonstrated that spatially distributed variables are sensitive to the spatial resolution of the GMF. The temporal resolution of the GMF impacts the dynamics of watershed responses. The choice of GMF depends on the quantity of interest and its spatial and temporal scales.
Using an integrated watershed model, we compared simulated watershed hydrologic variables driven...
